动态环境下移动机器人运动目标测速方法

提出了基于Kinect摄像机的 机器视觉测速方法,用于实时的测量运用目标的速度信息。首先,设计出固定在运动目标上 的由 五个已知位置关系的圆组成的标识物,利用图像处理算法根据标志物的拓扑关系检测出运动 目标标志物在Kinect坐标系下 的位置;然后,将Kinect坐标系坐标通过坐标变换转换到世界坐标系;最后,根据采样时间 内测出的距离求出速度信息。通过 以上3步骤,依次解决运动目标检测和运动目标测速问题。理论分析和试验结果表明, 本文方法在移动机器人运动或者静止 情况下能够处理运动目标作直线运动、原地转动及其合成的曲线运动等不同运动形式下的速 度测量,应用范围广泛,计算实 时性和准确性高。     

基于高维状态空间的移动机器人定位

位置跟踪是移动机器人自主导航中的一个主要任务.扩展的卡尔曼滤波定位方法是一个常用的位置跟踪方法,但是在对非线性系统方程进行线性化近似过程中引入了线性化误差.文中给出了一个基于线性系统模型的位置估计方法.用一个高维的状态向量表示机器人的位置空间,并选用环境路标的全局信息作为观测向量,此时系统动态模型和系统观测模型都是线性的,从而直接运用最优的线性卡尔曼滤波技术进行移动机器人位置估计.这种方法免除了非线性方程的线性近似过程,避免了线性化误差.实验表明,位置估计过程是收敛的、一致的.     

Determining the absolute orientation in a corridor using projectivegeometry and active vision

The capability of a mobile robot to determine its position in the environment (self-localization) is a prerequisite for achieving autonomous navigation. An approach is proposed for determining the absolute orientation of an autonomous robot in a system of corridors, based on the projective geometry and active computer vision. In the proposed approach, the common direction of longitudinal corridor edges is inferred by detecting the vanishing point of the corresponding straight line segments in the image. It is assumed that the knowledge about the vertical direction in the scene is available, so that the image coordinates of these vanishing points are considerably constrained. However, longitudinal corridor edges are not visible in images acquired for many viewing directions, so that the processing in a localization procedure has to be performed on a sequence of images acquired from the given position, for regularly arranged orientations of the camera. Extensive experimentation was performed on real scenes and the obtained results are provided     

基于时滞卡尔曼滤波的机器人视觉定位

在移动机器人视觉伺服跟踪系统中,考虑到视觉系统的延时,本文在引入单目视觉测量信息的基础上,利用延时的扩展卡尔曼滤波方法进行了空间运动目标定位和运动信息获取。蒙特卡洛仿真实验结果验证了该方法的有效性和可行性。     

Robot vision tracking system

A system designed to test a tracking theory for finding the position of an object in a scene, even when it is entering or exiting, is described. The design of a servo system used to move the camera while tracking the object is proposed. The technique involves the fundamental frequency of the Fourier transform of the vertical and horizontal projections of the image. This technique will work with stationary or moving objects as well as with a stationary or moving camera. The objective is to apply this technique to find the line between the camera and the object, such that the robot arm could follow that line until it encountered the object, and then seize it. This technique may also be useful in performing camera calibration     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

基于激光雷达的移动机器人运动目标检测与跟踪

运动目标的检测与跟踪是移动机器人研究的难点问题之一.基于2维激光雷达的性能分析与滤波设计的基础上,提出一种自主运动目标检测与跟踪的方法.通过k-近邻对激光雷达扫描的障碍进行聚类分析;基于栅格地图匹配的方法评估障碍的运动参数;检测出运动目标后,通过对机器人进行运动补偿,提出一种改进的粒子滤波算法对目标的运动状态进行评估.最后,利用自行研制的移动机器人进行算法评估,实验结果验证了所提方法的有效性.     

Teach by zooming: A unified approach to visual servo control

Traditionally, a visual servo control problem is formulated in the teach by showing framework with an objective to regulate a camera based on a reference (or desired) image obtained by a priori positioning the same camera at the desired task-space location. A new strategy is essential for a variety of applications where it may not be possible to position the camera a priori at the desired position/orientation. In this paper, a visual servo control approach, called “teach by zooming”, is formulated where the objective is to position/orient a camera based on a reference image obtained by another camera. For example, a fixed camera providing a wide area view of the scene can zoom in on an object and record a desired image for another camera. A non-linear Lyapunov-based controller is designed to regulate the image features acquired by an on-board camera to the corresponding image feature coordinates in the desired image acquired by the fixed camera in the presence of uncertain camera calibration parameters. The proposed control formulation becomes identical to the well-known teach by showing controller when the camera-in-hand can be located a priori to the desired position/orientation; thus enabling control in a wide range of applications. Experimental results for regulation control of a 7 degrees-of-freedom robotic manipulator are provided to demonstrate the performance of the proposed visual servo controller.     

RFID-based localization with Non-Blocking tag scanning

The simple and cheap passive RFID tag systems have recently been used for efficient localization of indoor mobile robots, where each RFID tag stores its own absolute position and the mobile robot carrying an RFID reader scans the RFID tags to localize itself. The available localization schemes using passive RFID systems do not consider the scanning delay which may cause location estimation error, especially when the robot moves at a high speed. In this paper, a new Non-Blocking scanning (reading) scheme is proposed to avoid collisions so that the scanning delay, and consequently the localization error, can be reduced. This scheme avoids collisions among tag replies by assigning tag IDs based on the FCA coloring scheme. Theoretical and simulation studies indicate that the new Non-Blocking scheme combined with the new tag arrangement pattern can achieve a good RFID localization performance in terms of both estimation error and scanning delay with reduction of tag/reader required complexities.     

DETERMINING POSE PARAMETERS WITHOUT PRIMITIVE-TO-PRIMITIVE CORRESPONDENCE

The problem of determining the pose of an object in 3-D space is essential in many computer vision applications. In this paper, a model-based approach for solving this problem is proposed. This approach does not require the knowledge of point-to-point correspondences between 3-D points on the model and 2-D points in the observed image. The spatial location of the object is iteratively estimated and updated from the values globally defined over the model image and the observed image.     

Robot guidance using standard mark

A fast single-view approach to determining the robot location using the standard mark is proposed. In the approach, a standard mark with triangle pattern and location code is used as the reference target. No prerequisite condition is imposed in grabbing the image, and only simple 3-D vector algebra is used in calculating the location. Therefore, the alignment error can be avoided and the speed of location determination can be very fast     

Rigid and non-rigid 3D motion estimation from multiview image sequences

Multiview image sequence processing has been the focus of considerable attention in recent literature. This paper presents an efficient technique for object-based rigid and non-rigid 3D motion estimation, applicable to problems occurring in multiview image sequence coding applications. More specifically, a neural network is formed for the estimation of the rigid 3D motion of each object in the scene, using initially estimated 2D motion vectors corresponding to each camera view. Non-linear error minimization techniques are adopted for neural network weight update. Furthermore, a novel technique is also proposed for the estimation of the local non-rigid deformations, based on the multiview camera geometry. Experimental results using both stereoscopic and trinocular camera setups illustrate and evaluate the proposed scheme.     

Sensitivity-based data fusion for optical localization of a mobile robot

Acoustic-based techniques are the standard for localization and communication in underwater environments, but due to the challenges associated with this medium, it is becoming increasingly popular to find alternatives such as using optics. In our prior work we developed an LED-based Simultaneous Localization and Communication (SLAC) approach that used the bearing angles, needed for establishing optical line-of-sight for LED-based communication between two beacon nodes and a mobile robot, to triangulate and thereby localize the position of the robot. Our focus in this paper is on how to optimally fuse measurement data for optical localization in a network with multiple pairs of beacon nodes to obtain the target location. We propose the use of a sensitivity metric, designed to characterize the level of uncertainty in the position estimate with respect to the bearing angle error, to dynamically select a desired pair of beacon nodes. The proposed solution is evaluated with extensive simulation and experimentation, in a setting of three beacons nodes and one mobile node. Comparison with multiple alternative approaches demonstrates the efficacy of the proposed approach.     

RFID-based mobile robot guidance to a stationary target

Retrieving accurate location information about an object in real-time, as well as any general information pertinent to the object, is a key to enabling a robot to perform a task in cluttered, dynamically changing environment. In this paper, we address a novel technique for the guidance of mobile robots to help them identify, locate, and approach a target in our daily environments. To this end, we propose a standard for the use of radio-frequency identification (RFID) systems and develop a prototype that can be easily installed in the existing mobile robots.Specifically, when an RF signal is transmitted from an RF transponder, the proposed RFID system reads the transponder-encoded data and simultaneously picks up the direction of the transponder using the received signal strength pattern. Based on the angle of signal arrival, we develop the guidance strategies that enable a robot to find its way to the transponder position. Moreover, to cope with multi-path reflection and unexpected distortions of the signals that resulted from environmental effects, we present several algorithms for reconstructing the signals. We demonstrate that an off-the-self mobile robot equipped with the proposed system locates and approaches a stationary target object. Experimental results show that the accuracy of the proposed system operating at a frequency of 315 MHz falls within a reasonable range in our normal office environment.     

探测器着陆小天体的自主光学导航研究

提出了一种用于探测器着陆小天体的自主光学导航方案.该方案利用探测器上的光学导航相机和激光测距仪,分别测量三个预先选定特征点的图像坐标和探测器到特征点的距离,由此得出探测器的相对位置,并利用广义卡尔曼滤波实时确定探测器的轨道.最后,通过数学仿真验证了这种自主光学导航方案的可行性,并在敏感器测量精度一定的情况下,通过仿真分析了小天体模型误差对导航精度的影响.     

Autonomous Mobile Robot Navigation Using Passive RFID in Indoor Environment

This paper proposes an efficient method for localization and pose estimation for mobile robot navigation using passive radio-frequency identification (RFID). We assume that the robot is able to identify IC tags and measure the robot's pose based on the relation between the previous and current location according to the IC tags. However, there arises the problem of uncertainty of location due to the nature of the antenna and IC tags. In other words, an error is always present which is relative to the sensing area of the antenna. Many researches have used external sensors in order to reduce the location errors, with few researches presented involving purely RFID driven systems. Our proposed algorithm that uses only passive RFID is able to estimate the robot's location and orientation more precisely by using trigonometric functions and the IC tags' Cartesian coordinates in a regular gridlike pattern. The experimental results show that the proposed method effectively estimates both the location and the pose of a mobile robot during navigation.      

A visual servoing algorithm using fuzzy logics and fuzzy-neural networks

A visual servoing algorithm is proposed for a robot with a camera in the hand to track a moving object in terms of image features and their variations, where fuzzy logics and fuzzy-neural networks are involved to learn feature Jacobian-based kinematic control law. Specifically, novel image features are suggested by employing a viewing model of the perspective projection to estimate the relative pitching and yawing angles. Such perspective projection-based features would not interact with the relative distance between the object and the camera, and, desired feature trajectories for learning the visually guided line-of-sight robot motion are obtained by measuring features by the camera in the hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a commercially provided function of linear motion, and then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories.To show the validity of the proposed algorithm, some experimental results are illustrated, where a four-axis SCARA robot with a BW CCD camera is used.     

基于视频去噪算法移动机器人视觉定位研究

提出基于视频去噪算法的移动机器人的视觉定位方法,应用运动补偿时域视频去噪算法对视频图像去噪,提取去噪视频图像的图像像素,用摄像机标定方法确定目标空间坐标,扩展卡尔曼滤波Matlab仿真算法对目标进行定位,有效地解决了视频图像的拖尾、重影等现象,实现目标动态定位。实验结果表明视频去噪效果良好,摄像机标定准确,目标定位精确度高,达到了预期效果。     

基于多分辨率动态轮廓线的面积测量与跟踪方法

提出了一种基于多分辨率动态轮廓线的物体表面积测量方法。该方法使用光电式图像瞄准系统采集被测物体的目标图像并构造该图像的高斯金字塔,并采用了多分辨率动态轮廓线且使其由初始位置向目标轮廓边缘收敛;根据基于B样条封闭曲线面积与形心公式,准确计算出图像的目标面积与形心;再利用形心自标定技术、图像目标形心以及双频激光器测量出像素的尺寸当量,从而准确测量出真实物体的表面积。实验表明,在动态轮廓线控制点数目为20时,其单次测量误差达到了±0.2%。与单分辨率动态轮廓线面积测量方法相比,该方法具有鲁棒性强,平均值误差、单次测量误差及重复性误差小等优点。将该方法应用于基于红外与可见光图像融合的运动目标跟踪过程的实验表明,它不仅具有很好的跟踪准确性,而且能够跟踪快速运动的目标。     

State estimation on flexible robots using accelerometers and angular rate sensors

Fast moving robots with elastic joints and/or elastic links require vibration suppression to achieve accuracy. We introduce a two-degrees-of-freedom control scheme (2DoF) consisting of a feedforward component based on the flatness approach and a classic feedback component, such as a PD motor joint control with additional link position error or joint torque error feedback. Such a control scheme requires knowledge of the full system state. We present an approach for state estimation using angular rate-and acceleration-sensors-mounted on each robot link-that can be used for any kind of elastic robot manipulator. We validate our theory by measuring a very fast trajectory and the influence of an external disturbance on an articulated robot with two flexible links and three joints.